Smart Structures Research Laboratory, Department of Civil, Structural and Environmental Engineering, University at Buffalo, The State University of New York, NY 14260, USA.
Ultrasonics. 2014 Feb;54(2):486-501. doi: 10.1016/j.ultras.2013.07.016. Epub 2013 Aug 2.
Nonlinear Kalman Filtering is an established field in applied probability and control systems, which plays an important role in many practical applications from target tracking to weather and climate prediction. However, its application for acoustic emission (AE) source localization has been very limited. In this paper, two well-known nonlinear Kalman Filtering algorithms are presented to estimate the location of AE sources in anisotropic panels: the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These algorithms are applied to two cases: velocity profile known (CASE I) and velocity profile unknown (CASE II). The algorithms are compared with a more traditional nonlinear least squares method. Experimental tests are carried out on a carbon-fiber reinforced polymer (CFRP) composite panel instrumented with a sparse array of piezoelectric transducers to validate the proposed approaches. AE sources are simulated using an instrumented miniature impulse hammer. In order to evaluate the performance of the algorithms, two metrics are used: (1) accuracy of the AE source localization and (2) computational cost. Furthermore, it is shown that both EKF and UKF can provide a confidence interval of the estimated AE source location and can account for uncertainty in time of flight measurements.
非线性卡尔曼滤波是应用概率和控制系统中的一个成熟领域，它在从目标跟踪到天气和气候预测等许多实际应用中都起着重要作用。然而，它在声发射 (AE) 源定位中的应用非常有限。本文提出了两种著名的非线性卡尔曼滤波算法，用于估计各向异性板中 AE 源的位置：扩展卡尔曼滤波 (EKF) 和无迹卡尔曼滤波 (UKF)。这些算法应用于两种情况：速度剖面已知 (CASE I) 和速度剖面未知 (CASE II)。将这些算法与更传统的非线性最小二乘法进行了比较。通过对碳纤维增强聚合物 (CFRP) 复合材料板进行实验测试，该板上安装了稀疏的压电换能器阵列，以验证所提出的方法。使用带有仪器的微型脉冲锤模拟 AE 源。为了评估算法的性能，使用了两个指标：(1) AE 源定位的准确性和 (2) 计算成本。此外，还表明 EKF 和 UKF 都可以提供估计的 AE 源位置的置信区间，并可以考虑飞行时间测量的不确定性。
